Token Classification
Transformers
PyTorch
JAX
Chinese
roberta
chinese
classical chinese
literary chinese
ancient chinese
bert
Instructions to use ethanyt/guwen-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethanyt/guwen-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ethanyt/guwen-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwen-ner") model = AutoModelForTokenClassification.from_pretrained("ethanyt/guwen-ner") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwen-ner")
model = AutoModelForTokenClassification.from_pretrained("ethanyt/guwen-ner")Quick Links
Guwen NER
A Classical Chinese Named Entity Recognizer.
Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to Guwen Models.
See also:
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ethanyt/guwen-ner")